Digital automation, and its impact on labor, society and the economy, has been studied from multiple perspectives and through many lenses. In his new research and analysis, Daron Acemoglu, the Elizabeth and James Killian Professor of Economics at MIT, acknowledges inequalities created when automation displaces certain human skills. However, he also says it is possible for new technology to create more complex versions of existing tasks where labor has a comparative advantage, tipping the scales back toward a future with plentiful jobs.

Acemoglu completed his graduate work in mathematical economics and econometrics at the London School of Economics, where he also received his Ph.D. in economics. His recent research focuses on the political, economic and social causes of differences in economic development across societies; the factors affecting the institutional and political evolution of nations; and how technology impacts growth and distribution of resources. Acemoglu has published four books:Economic Origins of Dictatorship and Democracy (joint with James A. Robinson), Introduction to Modern Economic Growth, Why Nations Fail: The Origins of Power, Prosperity, and Poverty (joint with James A. Robinson), and Principles of Economics (joint with David Laibson and John List).

He recently spoke at an MIT IDE seminar on the topic of, "The Race between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment.” IDE Community manager Paula Klein followed up with four questions. Below are his responses.

Q. The current rivalry between digital automation and humans seems focused on economics and labor issues— concerns that labor will be progressively marginalized and made redundant by new technologies. Is this focus premature or overstated?

A: It is certainly not premature. We have seen many different types of tasks produced and performed by labor, even fairly skilled labor, become automated over the last 30 years. We also know of new technologies that will automate some very major occupations (regulations permitting), including driving, airplane piloting, some aspects of surgery, certain types of diagnoses and even parts of the practice of law.

Yet there is an aspect of it that is overstated. These are still only some of the occupations that humans perform today. The more important overstatement comes as one turns from automation to the prospects for future employment creation. This rapid process of automation does not mean that the future economy will not create jobs. If you look at the last several decades, qualitative evidence suggests rapid automation has been going on for more than a century, and a lot of the new employment comes in new tasks and occupations. So, as machines take jobs previously performed by humans, the economy appears to create yet other tasks and jobs to employ the displaced workers.

Q: How does your task-based framework help explain the current economic situation and provide context? Can you briefly summarize the model and your research?

A: Our framework helps us understand the aforementioned patterns and why the fact that new employment will come from new tasks and activities. But more importantly, because it endogenizes the speeds at which existing tasks are automated and new tasks are created, it also highlights why a period of unusually rapid automation generally brings a subsequent period of rapid creation of new tasks. Put simply, rapid automation depresses the price of labor which has fewer tasks to work. This then makes it more profitable for new tasks, which employ new labor, to be created.

Q: How might these new tasks spur economic growth and innovation?

A: The growth implications of creating new tasks are essentially a corollary of what I have just described. Growth comes about both because of automation -- we can do things we have been doing more cheaply-- and because of the creation of new tasks; we have new goods and services using better technology. Anything that spurs innovation triggers faster economic growth. So, rapid automation is a double whammy: it benefits us directly and it spurs additional creation of further growth-enhancing new tasks.

Q: What guidance can you offer to employers, workers, students and policymakers to prepare and adjust for the Second Machine Age?

A: All of these scenarios are no consolation if you do not have the skills that new tasks and jobs will demand. Some economists are now questioning whether college is a good investment. There are certainly reasons for rethinking some of our long-cherished assumptions: college is expensive and college graduates have not done very well in the labor force over the last 15 years or so. Nevertheless, improving the skills of our workforce and improving our own skills still remain the only ways of ensuring that we adapt to the future of technology.

Online media and social-advertising necessitate new ways to measure and drive data-based decision-making among customers. They are also creating a new field of experimental learning techniques and tools that are replacing classic, randomized market testing practices in many cases.

Dean Eckles, a social scientist, statistician and assistant professor in the MIT Sloan School of Management, explained how tools for designing, deploying and analyzing online field experiments can encourage good statistical and methodological practices as well as better understanding of online customer behavior. As MIT Sloan professor Glen Urban and IDE researcher, Sinan Aral, and others, have discussed, different types of ads and messaging are being tested all the time to determine what motivates online marketing and social activities.

Eckles, a former member of the Core Data Science team at Facebook who also worked at Yahoo, knows first-hand that “the Internet industry has distinct advantages in how organizations can use data to make decisions. Firms can cheaply introduce numerous variations on the service and observe how a large random sample responds when randomly assigned to these variations.”

At the same time, he told a recent MIT IDE seminar, “rapid, iterative, and organizationally distributed experimentation” also introduces important challenges-- such as understanding the effects of a change intervention.

Challenges arise because many experiments are being run -- often by different teams -- requiring tools to support rapid experimentation. For example, "How can multiple different teams experiment with the design of a single page at the same time?"

PlanOut: An App for Experimental Design

Facebook was seeking alternatives to standard A/B tests to answer questions like these about its users. A/B tests work well when minor tweaks to a system are needed, but not when more complicated or nuanced change has to be measured. Eckles and his team team built an open-source app called PlanOut, a language for describing complex experimental designs for behavioral science experiments. It uses standard code script to assign value to specific procedures and also can help manage multiple testing that takes place simultaneously on a site. Less technical users can program it via a GUI.

Eckles is interested in other applications for these types of tools and analytics that might, for example, show the best way to motivate voting or civic participation.

Feedback is important on social media content, he said, and experiments can focus on straightforward items such as comment boxes to measure user engagement or they can analyze more subtle factors such as how comments affect users and influence others.

For more on Eckles’ research on PlanOut see his 2014 paper here and a list of work here.

When machine “workers” are on 24 x 7 shifts, how can humans compete? When autonomous drones can achieve tasks without human intervention, what are our moral responsibilities?

In the rush to bring newer, smarter and more capable technologies to market, few are addressing the ethical and moral dilemmas that automation has raised. Psychology professor Joshua Greene, Director of the Moral Cognition Lab at Harvard University, however, is starting to relate his research about the brain and human morality to the world of IT and robotics.

At a February 18 seminar hosted by the MIT IDE, Greene noted that until recently, he didn’t fully make the connection between his own work and the long-term issues of Artificial Intelligence (AI). That intersection becomes very clear, however, when you think about the real-world issues of job displacement, how machines are programmed and what they are instructed to do. (More about Automated Ethics can be found here and here.)

Drawing on insights from his 2013 book, Moral Tribes: Emotion, Reason, and the Gap Between Us and Them, Greene explained that we react most strongly to harmful actions like punching someone in the face, where the harm is caused intentionally and directly, and the victim is an identifiable person. The social and moral challenges posed by advancing AI are different. If advanced AI puts millions of people out of work it won’t feel like intentionally punching someone—or a million people. The harm will be caused as an indirect side effect of doing something good. And those affected will be “statistical” people rather than identified individuals. It’s this mismatch between our moral psychology and the consequences at stake that makes modern moral problems so challenging.

Greene believes more focus is needed on critical problems like whether--and how—moral sensibilities can be programmed into autonomous machines such as military drones and self-driving cars. On a larger scale, societies have to re-imagine the world as one in which machines do more and more of the work currently done by humans. Technological advances may soon outpace our own moral sensibilities, according to Greene. “We’ll need to find new solutions.”

Joshua D. Greene is Professor of Psychology, a member of the Center for Brain Science faculty, and the director of the Moral Cognition Lab at Harvard University. He studies the psychology and neuroscience of morality, focusing on the interplay between emotion and reasoning in moral decision-making. His broader interests cluster around the intersection of philosophy, psychology, and neuroscience. He is the author of Moral Tribes: Emotion, Reason, and the Gap Between Us and Them.

The same robotic technologies that enable laboratory droids to bake cookies and construct small buildings have the potential to dramatically automate manufacturing processes in the next decade.

Robots are already changing lives and accelerating productivity much as computing did in the last few decades, according to MIT professor and researcher, Daniela Rus. The next wave of exponential growth and advancements in robotic fabrication also will have a huge impact on the digital economy. “We will soon get to an age where it is as easy to have your own robots as it is to print on paper today,” she said at an October IDE seminar. Rus, Professor of Electrical Engineering and Computer Science as well as Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), discussed and demonstrated the results of her most recent work.

Based on a 2014 McKinsey report advanced robotics, the Internet of Things and autonomous cars are among the top 12 disruptive technologies with a total potential economic impact between $14 trillion and $33 trillion a year in 2025. (See infographics here). Advanced robotics alone could generate from $1.7 to $4.5 trillion, according to McKinsey. The estimates are "based on an in-depth analysis of key potential applications and the value they could create in a number of ways, including the consumer surplus that arises from better products, lower prices, a cleaner environment, and better health."

Rus said despite huge progress, the high cost of robotic design and production, as well as limitations in communications and physical dexterity still have to be overcome before bots can reach their full economic potential. Toward that end, her group is building and deploying easily designed robots that can perform complex, multi-step tasks. They can also interact with humans and follow instructions with new levels of precision and accuracy. For example, a team of bots in the lab have built a log cabin by identifying parts and language sequences, then dividing the tasks among themselves. Once at work, the robots analyze instructions and adjust their processes to accommodate their droid co-workers. They communicate with each other and can ask their human partners for specific help if they get stuck with a task as well.

In another example, an Iron Chef bot baked cookies by “reading” a series directions, mixing the ingredients and popping the pan into the oven. Executing such seemingly simple tasks required more than a year’s work in computational and lab development at MIT and cost about $500,000 to produce. The group is also testing a series of simple, self-assembling "origami" style robots. The results of all of these efforts clearly “stretch the boundaries of what robots can do” now and indicate how much more they can achieve in the very near-term, Rus said—especially when commercial developers stake their claim.

“The state of robot production today is similar to where programming was before the invention of compilers,” she said. And with the rapid pace of advancements, the proliferation of low-cost, high function robots is just a blink away.

Before Google Glass (which was first developed at MIT) and wearable computers were part of the general lexicon, Pattie Maes, professor and interim head of the Program in Media Arts and Sciences, pioneered these technologies at the MIT Media Lab.

Maes founded and directs the Media Lab's Fluid Interfaces research group and her 2009 TEDtalk on Sixth Sense technologies has more than 8 million views online.

At the MIT CDB/IDE annual meeting in May, Pattie discussed some of the group’s latest research in the areas of wearable/ubiquitous computers, smart objects, and the Internet of things and how these cutting-edge technologies are finding applications in consumer, business and industrial settings. Some clips from her presentation can be found below.

Exciting big data possibilities – as well as business intelligence and business analytics possibilities -- are all well and good, but what businesses really want is to deliver value from the massive amounts of data they have amassed over time. And most agree that the best way to demonstrate that value is to monetize it. But what exactly does that mean, and how can it be achieved?

These are among the key questions that Professor Barbara Wixom is attempting to address in her current research. Wixom joined MIT Sloan in June 2013 as a Principal Research Scientist for the Center for Information Systems Research (CISR). At a recent MIT IDE/CDB seminar describing her preliminary work, Show Me the Money: Delivering Business Value through Data, Wixom noted that: “In a digital economy, data, and the information it produces, is one of a company's most important assets. Increasingly, companies are monetizing their data assets and generating business value via existing core products and services or new digitized ones.”

For Wixom’s current study, she interviewed more than 50 business leaders involved in data monetization efforts and discovered that definitions of data monetization varied widely – ranging from selling data products and services for revenue generation, to exploiting data internally to drive tangible bottom-line results. When a company explores data monetization with the latter intent in mind, Wixom noted that data providers are good companies to use as role models. Because data monetization is at the core of their business models, data providers have learned over the years how to be really good at monetizing.

Provider, non-Provider Examples

Wixom studied comScore, a 14-year-old marketing research firm “with 14 petabytes of online data, collected real time from around the world.” In a research paper earlier this year, she describes how comScore achieves value creation from big data via three key assets: “A cost-efficient, scalable platform; an analytics-savvy workforce; and a deep understanding of its clients.” She concluded: “Data and analytics providers are highly experienced at working with big data. They create, build, and hone capabilities to exploit their data assets.”

Wixom also discussed the evolution of one her early case studies: medical supply distributor Owens & Minor. Although the company’s core business is distribution, Owens & Minor has a long history of gathering, using and ultimately monetizing its data via its “spend analytics” products and services. Since the 1990’s the distributor has collected information from its supply chain and sold it to suppliers that wanted to increase market penetration and sales – and to customers that wanted to manage cost of patient care. Over the next decade, Owens & Minor won new business and generated revenue as a result of its unique analytics capabilities. In addition to hard-dollar gains, it earned brand and reputational benefits as an early technology leader and consulting partner in the healthcare industry. Nevertheless, Owens & Minor’s analytics offerings now must co-exist and compete with offerings from software vendors, consulting firms and group purchasing organizations, Wixom said.

The bottom line to both the comScore and Owens & Minor stories, according to Wixom is this: Data monetization is not easy. As companies consider selling their data, they need to get into the game with eyes wide open, she adds.

MIT Sloan Professor Scott Stern’s latest research draws a clear correlation between the elements present at the founding of entrepreneurial startups and their later success. In addition, he and MIT doctoral candidate Jorge Guzman, use other widely available data-- such as incorporation information, patents, trademarks, IPOs and venture capital funding-- to measure and identify the potential for future growth.

The findings of the study, “Nowcasting and Placecasting Growth Entrepreneurship,” were presented at an MIT IDE seminar in March by Stern, who is Professor of Management of Technology and Chair of the Technological Innovation, Entrepreneurship and Strategic Management Group at the MIT Sloan School of Management. He and Guzman were not only able to draw conclusions, but to observe data-documented entrepreneurial trends, using algorithms and estimation models. These can “help us understand the origins and dynamics of startups,” Stern said.

Shifting Growth Patterns

Placecasting can be used to “evaluate the role of regional ecosystems” in the growth—and decline -- of startups, and to identify clusters of “hyperinnovation.” “Our approach allows us to track the changing locational patterns of growth entrepreneurs over time,” and in real-time, he said, as opposed to traditional, static survey methods. For example, “in Massachusetts, we are able to document the transition from Route 128 growth entrepreneurship to clustering in Kendall Square in Cambridge and Boston.” Similarly, in California, he is tracking the move of entrepreneurship from Silicon Valley to San Francisco.

Using what he calls nowcasting, Stern expects to develop a predictive model of growth outcomes and assign a probability of growth based on current developments and past indicators. It will also be easier to spot and evaluate why some firms will not succeed. Going forward, Stern also understands that the pace of change and the “app economy” will require new criteria and there will be new shifts to track.

Stern works widely with both companies and governments in understanding the drivers and consequences of innovation and entrepreneurship, and has worked extensively in understanding the role of innovation and entrepreneurship in competitiveness and regional economic performance. For more about regional clusters, watch this video and for more on the research, contact Stern at sstern@mit.edu .

Can workers actually be beneficiaries of the digital economy? MIT Sloan Professor Zeynep Ton believes the answer is yes. And just as importantly, she says, businesses won’t lose out in the process.

Much has been written and discussed about the economic inequalities created as a result of digital technologies. In this blog, for example, MIT Research Scientist, Andrew McAfee, cites significant economic data supporting the view that IT is responsible for tectonic changes in U.S. jobs and wages. Professor Ton’s latest research offers some win-win scenarios for employees and their employers.

In a recent presentation, Ton, Adjunct Associate Professor of Operations Management (pictured at left), went beyond defining the problems of job displacement, dissatisfaction and despair; she offered solutions. Her latest book, “The Good Jobs Strategy,” examines ways to bridge some of the widely acknowledged economic gaps and suggests ways that organizations “can design and manage their operations in a way that satisfies employees, customers and investors simultaneously.”

Tossing Out Conventional Wisdom

Ton asserts that currently, one in four workers – especially in the service sector and retail—has a “bad job” where salaries are insufficient to support families, and work is rote, irregular and unsatisfying. In such environments, “workers are set up to fail.” But businesses and society fail as well, she maintains. “The conventional wisdom is that bad jobs are necessary to keep costs low and profits high. Even advocates for higher wages believe higher wages they will come at a cost—either higher prices for customers or lower profits for companies.”

However, better operational strategies can break the pattern, she says. It may be counter-intuitive, Ton explained at a meeting of the MIT IDE in March, but combining investment in people with smart decisions like empowering workers—not cutting back—often proves most profitable.

In case after case, she found that more and better-trained and motivated staff can generate higher profit and growth and help business stay ahead of competitors.

Zara, Mercadona and QT Find Win-Win Formulas

“It’s a virtuous cycle,” Ton says: “Good execution and good workers yield more profits.” For example, Zara clothing and the Mercadona supermarket chain, both based in Spain, are growing despite a weak economy. Mercadona offers employees twice the minimum wage, bonuses, stable work, full-time schedules and opportunities for growth.

In the U.S., QT, or Quiktrip, is an example of a convenience store/gas station company providing “excellent customer service, fast, clean facilities and a high employee retention rate. People want to work there,” Ton says, and store profits are above industry averages. Trader Joe’s and Costco are other good examples.

How do they do it? Ton offers a matrix four strategies that need to be used in combination to reap the greatest rewards:

1. Invest in people and combine that with operational excellence to drive sales.

2. Standardize processes to increase efficiency and empower employees to make decisions for customers

Ton sums up as follows: “In my book, The Good Jobs Strategy, I show that it is possible to offer good jobs to workers, low prices and excellent service to customers, and great returns to shareholders-- all at the same time. What makes good jobs not only possible but very profitable—even in low-cost service businesses—is a set of counterintuitive choices that transforms the company’s investment in workers into high performance. What are these choices? Offer less, combine standardization with empowerment, cross-train, and operate with slack. It’s a combination that lowers operating costs, increases worker productivity and puts workers at the center of a company's success.”

Biography:

Ton’s work has been published in a variety of journals, including Organization Science, Production and Operations Management, and the Harvard Business Review. In addition, she has written numerous cases that explore different approaches to managing retail stores and labor. Prior to MIT Sloan, Ton spent seven years as an assistant professor in the Technology and Operations Management area at Harvard Business School, where she was awarded the HBS Faculty Teaching Award for teaching excellence.

Ton holds a DBA from Harvard Business School and a BS in Industrial and Manufacturing Engineering from Pennsylvania State University.

As social media becomes more pervasive in business and economic life, many researchers are trying to figure out just how much impact it is having on sales and on business models.

Jeffrey Hu, Associate Professor at Scheller College of Business at Georgia Institute of Technology, and an MIT Sloan alumni, is among those studying the effects of social media. In particular, he examined just how much online broadcasting channels and crowdsourcing are influencing markets and customers compared with more traditional marketing channels. “With the emergence of social media and Web 2.0, broadcasting in the online environment has evolved into a new form of marketing due to the much broader reach enabled by information technology,” Hu said.

Turning Buzz into Business

During 2008 to 2009, Hu studied the patterns of the MySpace music community, the largest at the time, with 14 million users. He wanted to know if broadcasting information via social media –sending updates, bulletins and texts (this was before Twitter really had a strong presence) would result in greater economic returns. In other words, he said at a recent MIT CDB lunch seminar, “whether buzz could turn into sales.”

Hu and his team employed a panel vector auto-regression (PVAR) model to investigate the inter-relationship between broadcasting promotions in social media and music sales. By correlating social media activity of 631 musicians for 32 weeks and comparing the data to Amazon rankings, Hu was able to see a significant effect on sales. The study accounted for control variables such as promotional spending, new album releases and size of network, among other factors.

The research concludes that artist-generated content -- particularly personal messages versus automated ones-- can increase sales and ranking on Amazon. By extension, Hu believes that companies can use social media to promote products and boost sales. “Our findings also point to the importance of conducting captivating conversations with customers in the organizational use of social media,” he said.

The Wisdom of Crowds

The second study Hu described at the seminar looked at the wisdom of crowds and crowdsourcing compared with expert advice and content online. Many people have pointed out that while Wikipedia contains errors, for example, it also can be corrected quickly from a vast range of sources versus traditional, permanent resources such as print encyclopedias. Some advocates believe that customers turn to peer-based communities, such as Yelp, for restaurant reviews over venerable sources like Michelin guides because the websites are more current, are more accessible and have wider coverage areas.

In his research of the financial analysis sector, Hu found that the online community Seeking Alpha--which relies on investor input instead of journalists or professional analysts-- has been “surprisingly accurate” in predicting financial trends and making investment recommendations.

Of course, there are also many caveats where enterprise social media is concerned. As McKinsey notes in this recent journal article, “on-demand marketing” is putting enormous pressures on businesses to respond in four key areas:

1. Now: Consumers will want to interact anywhere at any time.

2. Can I: They will want to do truly new things as disparate kinds of information (from financial accounts to data on physical activity) are deployed more effectively in ways that create value for them.

3. For me: They will expect all data stored about them to be targeted precisely to their needs or used to personalize what they experience.

4. Simply: They will expect all interactions to be easy.

Maybe the next studies will focus on how well social media can help achieve these daunting consumer demands.

For related MIT research about social advertising, see this blog describing Catherine Tucker’s research.

Most discussions and examples of business innovation focus squarely on the production side; how new products are created, built and marketed to improve efficiency and move the technology dial forward.

What gets lost in this classic view, however, is the human capital impact resulting from disruptive innovation, according to Michael Schrage, research fellow at MIT Sloan School’s Center for Digital Business. And, he asserts, that misrepresents innovation’s real role.

“Innovation is not just about faster, better, cheaper products and services, but an investment in the human capital and capabilities of customers and clients,” he told faculty at a recent lunch seminar on the topic of “Misunderstanding Human Capital,” at the CDB. “The transformative effect on human capital” is largely misunderstood or absent when innovation’s value is considered, Schrage argues. “Successful innovations transform users and customers” not just production processes, he asserts.

At the lively, interactive lunch session, Schrage noted that when Ford first mass produced automobiles he also facilitated “the mass production of drivers” – a form of human capital that hadn’t really existed before.

Similarly, Google created searchers, not just search engines, disrupting the way consumers interacted and adding value to Google’s algorithms. Walmart trained consumers to look for low prices, while Tesco focused on customer loyalty. Schrage wants to call attention to what he terms the “misunderstanding of mass production’s significance on human capital,” and to study the consequences – positive and negative -- of these actions on consumers.

This thinking borrows from two-sided market theory and platform economics where creation of one new market can lead to the creation of a new, complementary network such as iPhone apps, Schrage says.

Schrage’s latest research seeks to demonstrate that “consumptionary capital” is an important aspect of innovation that has been widely overlooked. In fact, technological innovation often creates and enhances human capabilities and increases competencies that can generate new business opportunities. Eyeglasses were technical innovations that made it possible and affordable for more people to read and see; hearing aids and cochlear implants were technical innovations that made it possible for more people to hear. They expanded the abilities and capabilities of their users to do and consume more, he says.

Asking customers to do something different doesn't go far enough. Serious marketers and innovators must ask customers to become something different instead. Even more, you must invest in their capabilities and competencies to help them become better customers.

A primary insight of the book is that innovation is an investment in your clients, not just a transaction with them, and that transforming customers will transform the business.

By addressing questions such as: How will skills change as a result of Google cars? How do innovations such as recognition engines and crowdsourcing impact consumer habits, behavior and income? How are new consumer norms created? –the research may help businesses better understand their customers. From that, they can adopt more meaningful marketing and product development plans that will yield more -- and more valuable -- customers.

Successful business innovators like Henry Ford, Steve Jobs and a Jeff Bezos, he argues, have clear visions of who they want their customers to become.

Whether you think it’s a good idea or not, mobile and social technologies are creating new ways to follow, analyze and predict how people are “embedded in society,” and how and where they spend their time and money. The implications of these changes for individuals, as well as society, are being studied by Alex `Sandy’ Pentland, director of MIT’s Human Dynamics Laboratory and the MIT Media Lab Entrepreneurship Program.

His current research examines four ways that Big Data can help to understand human behavior: By modeling social influence; by examining social influence dynamics; by actually shaping behavior, and by creating more data-driven societies. Pentland hopes these insights may help reverse “many of the frustrating phenomena that we are familiar with....fads, groupthink, and projects that just go nowhere.”

The MIT researchers looked at social influence networks and their relationship to learning, purchases and other behaviors by following 65 young families for one year. One finding was that social influence incentives work to change behavior more than other incentives because in a group, members have common ties and an exchange network on which to rely. Local information can pressure peers to act in certain ways and to be rewarded for those behaviors. “Incenting the social ties can be efficient,” Pentland explained at a recent MIT CDB seminar.

In call centers, for example, productivity improves with more coffee breaks, because workers share information that leads to better performance. Similarly, “social traders who aren’t isolated and aren’t in echo chambers,” perform best, he said. The point is to “encourage diversity of ideas and engagement.”

The Human Dynamics Lab at the MIT Media Laboratories pioneered the idea of a society enabled by Big Data. The Lab has developed technologies such as reality mining, which uses mobile phone data to extract patterns that predict future human behavior, as well as a `nervous system’ framework for dramatically more efficient transportation, health, energy, and financial systems.

Pentland’s latest research could be applied to what he calls, “data-driven societies.” Since geography influences behavior and patterns of communications, which creates “collective intelligence” in local groups,” city-scientist, for instance, may be able to predict the GDP of a city by looking at social-tie patterns. In turn, this might help city planners build environments that better match the habits of the local citizens.

Separately, McKinsey is conducting research into social intelligence. In its new report, McKinsey discusses social intelligence as a means of guiding better business decisions.

The report states that by tapping into social platforms, businesses can gather and harness employee knowledge.

Today, many people who have expert knowledge and shape perceptions about markets are freely exchanging data and viewpoints through social platforms. By identifying and engaging these players, employing potent Web-focused analytics to draw strategic meaning from social-media data, and channeling this information to people within the organization who need and want it, companies can develop a “social intelligence” that is forward looking, global in scope, and capable of playing out in real time.

This isn’t to suggest that “social” will entirely displace current methods of intelligence gathering. But it should emerge as a strong complement. As it does, social-intelligence literacy will become a critical asset for C-level executives and board members seeking the best possible basis for their decisions.

And in another reportCapturing Business Value with Social Technologies, McKinsey conducted an in-depth analysis of four industry sectors that represent almost 20 percent of global sales.

[The analysis] suggests that social platforms can unlock $900 billion to $1.3 trillion in value in those sectors alone. Two-thirds of this value creation opportunity lies in improving communication and collaboration within and across enterprises. Frequently, these improvements will go well beyond the areas many companies have focused on to date in their social-media efforts: connecting with consumers, deriving customer insights for marketing and product development, and providing customer service.

Clearly, Pentland’s work supports McKinsey’s conclusion that: “Social technologies are destined to play a much larger role, not only in individual interactions, but also in how companies (and Pentland might add, societies), are organized and managed.”

Sandy Pentland is a member of this community. Comment on his work here.